Aiming at the problem that wind power prediction models under high-fluctuation scenarios struggle to balance point-value accuracy and interval reliability, a hybrid prediction model integrating parameter optimization and nonlinear quantile regression is proposed. First, a combined TCN-GRU-DA prediction model based on a dual attention mechanism is constructed, using feature attention to mine the spatial correlation of multidimensional meteorological features and it with combining multi-head attention to capture the temporal dependence of power sequences. Second, the improve secretary bird optimization algorithm is proposed to realize the intelligent optimization of the four hyper-parameters of the combined model. This algorithm significantly enhances convergence performance by integrating good point set theory and quantum computing initialization, time-segmented nonlinear weighting, the directional search mechanism of the northern goshawk optimization algorithm, and a Cauchy distribution strategy to enhance global search capability. Finally, a multi-head attention-based nonlinear quantile regression model is developed, which dynamically adjusts feature weights under different quantiles through an adaptive loss function, thereby improving the accuracy of conditional quantile estimation. Experimental results demonstrate that, for point prediction, the proposed model reduces MAE and RMSE by 33.33% and 31.93%, respectively, compared to TCN-GRU. For interval prediction, at a 95% confidence level, the PICP improves by 3.97% and PINAW decreases by 20.76%. The study confirms that the proposed model effectively addresses the synergistic optimization of point estimation and interval estimation for wind power prediction. It not only enhances prediction robustness under extreme weather but also provides multi-dimensional decision support for day-ahead scheduling and real-time control in power grids.